Editorial Type:
Article
Article Type:
Research Article

Automatic Identification of Three-Body Scatter Spike Based on Jensen–Shannon Divergence and Support Vector Machine

Lei Meng aKey Laboratory of Hunan Meteorological Disaster Prevention and Mitigation, Changsha, Hunan Province, China
bChina Meteorological Administration Training Center Hunan Branch, Changsha, Hunan Province, China

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Youwei Sang aKey Laboratory of Hunan Meteorological Disaster Prevention and Mitigation, Changsha, Hunan Province, China
bChina Meteorological Administration Training Center Hunan Branch, Changsha, Hunan Province, China

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Jia Tang aKey Laboratory of Hunan Meteorological Disaster Prevention and Mitigation, Changsha, Hunan Province, China
cHunan Meteorological Observatory, Changsha, Hunan Province, China

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Online Publication:
11 Sep 2024
Print Publication:
01 Sep 2024
DOI:
https://doi.org/10.1175/JAMC-D-24-0024.1
Page(s):
981–996
Restricted access

Abstract

The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution.

Significance Statement

In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Youwei Sang, ywsang@163.com

Abstract

The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution.

Significance Statement

In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Youwei Sang, ywsang@163.com
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Journal of Applied Meteorology and Climatology

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